CA Algorithm Theoretical Basis Document - KMA · 2019-07-18 · In order to acquire high qualitydata , The digitized retrieval of ground observation CA is requested through satellites

Algorithm Theoretical

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National Meteorological Satellite Center

CA Algorithm Theoretical Basis Document

NMSC/SCI/ATBD/CA, Issue 1, rev.4

26 December 2012


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REPORT SIGNATURE TABLE

Function Name Signature Date

Prepared by Yong-Sang Choi,

Heaje Cho 26 December 2012

Reviewed by Yong-Sang Choi 26 December 2012

Authorised by NMSC 26 December 2012


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DOCUMENT CHANGE RECORD

Version Date Pages Changes Version5 2012.12.26 - -Nothing has changed for contents besides ATBD form.


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Table of contents

1. Overview

2. Background and purpose

3. Algorithm

3.1 Theoretical background and basis

3.2 Retrieval method

3.2.1 Look up table retrieval method

3.3 Retrieval process

3.3.1 CA Retrieval

3.4 Validation

3.4.1 Validation method

3.4.2 Validation data

3.4.3 Temporal and spatial collocation method

3.4.4 Validation result analysis

4. Analysis method of retrieval results

5. Problems and possibilities for improvement

6. References


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List of Tables

Table 1 : Cloud top pressure and cloud bottom height corresponding to cloud type.

Table 2 : Detailed output data for the CA algorithm.

Table 3 : QC flag.

Table 4 : Validation results of CF and CA

Table 5 : Detailed output data for the CA algorithm.

Table 6 : Quality test result for the CA algorithm.


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List of Figures

Figure 1 : Images of (a), (b)nadir-view cloud fraction and (c), (d)fractional sky cover.

Figure 2 : Illustration of the parameters.

Figure 3 : Flowchart of CF and CA algorithm.

Figure 4 : Validation results of cloud fraction(a) and sky cover(b) for SGP site. Larger

circles in b represent improver Nhemisphvalues compared with Nnadir

Figure 5 : monthly mean bias between ground measured cloud fraction and MODIS

in the site.

retrieved cloud fraction (algorithm-retrieved sky cover).


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List of Acronyms

ARM Atmospheric Radiation Measurement

CA Cloud Amount

CF Cloud Fraction

CMDPS COMS Meteorological Data Processing System

COLL Collocation module

COMS Communication, Ocean, and Meteorological Satellite

CTP Cloud Top Pressure

GTS Global Telecommunication System

IPCC Intergovernmental Panel on Climate Change

ISCCP International Satellite Cloud Climatology Project

KMA Korea Meteorological Administration

MOD06 MODIS Cloud product

MODIS Moderate Resolution Imaging Spectroradiometer

RMSE Root Mean Square Error

SEVIRI Spinning Enhanced Visible and Infrared Imager

SGP Southern Great Plains

TSI Total Sky Imager

VAM Validation Module


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1. Overview

Cloud amount (CA) is a meteorological factor which deeply influences our daily life in areas such

as sailing, aviation, agriculture, and outdoor activities. Cloudiness in an atmosphere adjusts the

intensity of incoming solar energy. This in turn regulates the global climate system. It is also a key

parameter in adjusting the emission for terrestrial radiation at the top of the atmosphere. CA is one of

the essential objects of weather forecasting, so it has long been derived by ground measurements. Due

to the importance of CA in daily life over the Korean Peninsula, it has become an object of attention to

the general public. This includes the CA of surrounding countries and bodies of water (Oh et al. 2005).

In order to acquire high quality data, The digitized retrieval of ground observation CA is requested

through satellites because of the limitations of ground observation. This section will explain the CA

algorithm for satellite observation CA and eye measurement CA including the steps and processes

involved.

2. Background and purpose

The algorithm extracts CA viewed by satellites using pixel information for the existence or

nonexistence of clouds retrieved by scene analysis. When compared with other satellites, satellite

observation CA extracts data through averaging Moderate Resolution Imaging Spectroradiometer

(MODIS) 5x5 data, Spinning Enhanced Visible and Infrared Imager (SEVIRI) 3x3 data, and (IPCC)

1x1 data. Satellite observation CA is unaffected by the location and size of clouds, but these factors

significantly affect ground observation CA. Therefore, to retrieve both satellite observation CA from

COMS Meteorological Data Processing System (CMDPS) and ground observation CA generates the

best data. CA is an essential part of the equation to predict the maximum and minimum temperature.

However, this currently relies on the ground CA observations of human observers. It has the

limitations of objective reliability with time restrictions.

Satellite observation CA from Communication, Ocean, and Meteorological Satellite (COMS) and

ground observation CA can contribute to quantitative weather forecasting. It can also be utilized in a

digital forecast by providing information with specific number and graphic form in time and space.

This product can also be utilized for the study of an indirect impact of aerosol, along with effective


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particle radius of clouds.

3. Algorithm

3.1 Theoretical background and basis

Ground-observed CA is to be retrieved using inversion of method proposed by Kassianov et al.

(2005). Figure 1 illustrates the difference between ground-observed CA and satellite observation CA.

Figures 1(a) and (b) show real cloud distribution. Figures 1(c) and (d) show what can be observed

from the ground.

When many clouds exist at the zenith of ground observations without many clouds around them,

ground-observed CA can be overestimated. While clouds do not exist at the zenith of ground

observations with many clouds around them, ground-observed CA can be underestimated.

The differences between satellite-observed CA and ground-observed CA are caused by the

following:

First, there is a difference of observation conditions. Satellite observations detect the upper level

of clouds from outside of the Earth's atmosphere. Ground-observed CA detects the lower level of

clouds from the ground. Also, Satellite data has the value of pixels, but ground-observed data must

consider the curvature of the earth.

Second, ground-observed CA is affected by the horizontal and vertical structures of clouds, while

satellite observation does not consider these factors. To retrieve satellite-observed data, this

difference must be corrected. Therefore, we need to convert the results of satellite observations to

ground-observed CA. The physical law can be used to convert by the method shown in Kassianov et

al. (2005) and Oh et al. (2006).


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Fig. 1. Images of (a), (b)nadir-view cloud fraction and (c), (d)fractional sky cover (Kassianov et al., 2005)

3.2 Retrieval method

3.2.1 Look up table (LUT) retrieval method

With the observer at the center, we made a look up table (LUT) for weighting values depending on

the location of clouds. The weighting value is defined by the angle ( ) between the cloud and

observer locations for maximum zenith angle in view of the observer. This weight value must be

applied to each pixel from the zenith to α=α*.For example, in the case of a cloud located over the

zenith, the weighting value becomes 0 because it does not need to consider the weighting value due to

the cloud's location. However, in the case of a cloud located around the edge, the same cloud tends to

be more overestimated than those located at the zenith. In the α* of the maximum angle increasing the

weighting value to 1.

In the case of several pixel groups being selected, the most consistent results with ground-observed

CA investigate the sensitivity and have MODIS CA and ground-observed CA of the Southern Great

Plains (SGP) region of the Atmospheric Radiation Measurement (ARM). In this process, ground

observers used a cloud base height of 2km on the assumption that they looked at cloud base.


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The distance up to the edge of the clouds from the zenith of ground observers becomes about 12km,

either direction to them at the center becomes about 24km. The size of one pixel is 5km in the case of

MODIS. The edge seen by the observers needsfive pixels, one pixel in the middle, and two pixels each

direction. Thus, the observation area located at a certain point is 5x5 pixels.

When it is taken to 50km the maximum distance visible to an observer, increasing pixel size up to

15x15 and calculated for correlation coefficient and Root Mean Square Error (RMSE) with ground

observation data.Considering both the time for retrieving the ground-observed CA and consistency

with ground observation data, 5x5 appears to be the most suitable.

Table 1. Correlation coefficients and RMSE of sensitivity test to the cloud weight part.

R R5×5 R7×7 R9×9 R11×11 R13×13 15×15

SGP Correlation 0.94 0.94 0.93 0.92 0.90 0.90

RMSE 13.34 13.57 14.00 14.88 16.16 17.25

According to the sensitivity results for cloud weight value, COMS is 3km per pixel. The lookup

table to apply the weight value due to cloud position is a pixel group of 7x7 for ground-observed CA.

The retrieval method is as follows:

program makelut

!-------------------------------------------

!!Description :

! This subroutine is matrix to give cloud weight,

! == > Calculate matrix

! from formula of EVGUENI KASSIANOV et. al.

! IF fof Maximum degree(80)=distance (D) and a degree=d

! tan a = d/H

! # according to resolution, H is changed

! because tan80 = D/H, D is related to resolution

! Thus, a=tan-1(d/H)


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! cloud weight= a/80

!-------------------------------------------

implicit none

integer, parameter :: grid=3, pi=3.1452, res=3

real, parameter :: H=1.6

integer :: m, n

real, dimension(7,7) :: mata, matrix

open (1, file='lut_matrix.txt', status='unknown')

DO n= 1, grid*2+1 ; DO m= 1, grid*2+1

mata(m, n)= (grid+1-m)**2 + (grid+1-n)**2

mata(m,n)= ((mata(m,n))*(res))**(0.5)/H

matrix(m,n)= ATAN(mata(m,n))

matrix(m,n)= ((matrix(m,n)) * 180.) / pi

matrix(m,n)= matrix(m,n)/80.

IF (matrix(m,n)>=1.) THEN

matrix(m,n) = 1.

ELSEIF (matrix(m,n)<1.) THEN

matrix(m,n) = matrix(m,n)

END IF

END DO ; END DO

write (1,*) matrix

end program makelut

3.2.2 Methodology

This algorithm retrieves satellite-observed CA of an interim product that converts from % unit to

ratio including pixels of clouds from the results of cloud detection. In other words, cloud pixels under

confidence level of some clear are 1. After summing up to convert into 0 in clear pixels will divide


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into the number of total pixels.

The weighted value of ground-observed CA retrieved for position information of clouds from the

results of cloud detection and correcting differences that occur due to the geometric structure of clouds.

Fig. 2.Illustration of the parameters.

First, the correction for cloud position information is performed through the following process:In

the case of clouds located over the zenith, the weight value need not be considered according to cloud

position. The weight value of clouds are 0. However in the case of clouds located at theedge(i.e.,

α=α*= 80°), the same clouds tended to be overestimated than the zenith and the weight value becomes

1. Therefore, in cases of clouds locatedbetween the zenith and edge, the weight value isbetween 0 and

1.

After deciding the presence of clouds based on cloud detection information for basic input into the

algorithm, the cloud is assigned the weight value depending on ground observers and the position of

cloud based on the pixels.

In order to obtain the weight value depending on geometric information of clouds, it considers the

horizontal area and vertical height of clouds through the following process: The CA(Nnadir(α))

observed from satellites is defined as equation (1) for horizontal area that is occupied by clouds for the

area of a circle with a radius of R(α)(Figure 2). Ground-observedCA(Nhemisph(α)) is dependent upon

the zenith angle from the area occupied by the cloud for the solid angle.


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(%) (1)

(%) (2)

Where Scld,nadir(α) = horizontal cloud area (km), Snadir(α) = total area of the circle (km), Scld,hemisph(α) =

fraction of the solid angle filled by clouds (sr), Shemisph

(α) = observed solid angle with cone zenith

angle 2α(sr) are defined. The relationship between CA and ground-observed CA can be expressed as

equation (3).

(3)

Where = cloud weight, = cloud aspect ratio, and , in this algorithm the observer

supposed 80°to be the maximum zenith angle.

In order to retrieve the cloud aspect ratio the horizontal and vertical size must be considered.First

of all, the horizontal area(D) is calculated considering Nnadir

Cloud type is based on ISCCP, which is divided into low, medium, and high clouds depending on

the CTP. This criteria is listed in Table 2. Bottom cloud height is determined depending on the criteria

listed in Table 2. The cloud geometrical thickness is obtained by subtracting the cloud bottom height

from the cloud top height. Thus, in low, medium, and high clouds, median CTP pressures of 300, 500,

and 850 hPa are applied, respectively. The geometrical thickness for horizontal area of the retrieved

cloud is defined by the cloud aspect ratio.

retrieved into pixel unit and area of pixels.

When considering the vertical height (H) of clouds, after dividing into low/medium/high clouds,

Cloud Top Pressure (CTP) is used to determine the geometric thickness.

The algorithm consists of three parts retrieved by ground observed CA: Weighting value of the

cloud, cloud-observed CA, and cloud aspect ratio.Ground observed CA is retrieved in cloud/clear

conditions of averaging the surrounding 7x7 pixels converting by the method of Kassianov et al.


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(2005). More detail contents can be found in pages 2-3 of the appendix.

Table 2. Cloud top pressure and cloud bottom height corresponding to cloud type.

Level Genera Cloud top pressure

(hPa) Cloud bottom height

(km)

High Cirrus, cirrocumulus, cirrostratus 50-440 8

Middle Altocumulus, altostratus, nimbostratus

440-680 4

Low Cumulus, stratocumulus, stratus cumulonimbus

680-1000 1

3.3 Retrieval process

3.3.1 CA retrieval

The retrieval process of this algorithm is presented in Figure 3. The necessary input data to

estimate Satellite-observed CA and ground-observed CA are divided into basic input data and cloud

analysis data.

The basic input data is the weighting value information of clouds depending on cloud detection,

scene analysis data, and the position of the observer.

Cloud analysis data needs CTP data.

After deciding the presence of clouds based on cloud detection information, the cloud is assigned

the weight value depending on ground observers and the position of cloud by pixel detection. CA of

satellite (Nnadir

After inputting the weighted value of the clouds, cloud-observed CA, and cloud aspect ratio,

ground-observed CA is retrieved.The final product for ground-observed CA of cloud/clear condition

was generated by averaging the surrounding 7x7 pixels and converting by the Kassianov et al. (2005)

method.

) is calculated on the average cloud/clear pixel of the surrounding 7x7. We calculate the

cloud aspect ratio(γ) considering the horizontal and vertical size of cloud.


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Fig. 3. Flowchart of Cloud Fraction (CF) and Cloud Amount (CA) algorithm

In order to determine satellite-observed CA, the test deciding clear sky confidence level including

scene analysis has to be preceded. If clear confidence level is too high, the number of pixels classified

according to clear pixel aredecreased and the CA is increased.If clear confidence level is too low, the

number of pixels classified according to clear pixel are increased and the CA is a decreased. The case

analysis is compared with visible image data from the COMS algorithm experiment test in order to

decide a suitable clear confidence level need. The confidence level will be within 95%.

3.3.2. QC Flag

The position information of clouds and the horizontal and vertical size of clouds influence

retrievalof ground-observed CA. Therefore, it presents each QC flag for two processes, which is

shown in table 3. First, it presents up to 96-240 for the ratio of weight value (i.e., weight_rate=sum of

cloudy pixel weight / total weight in 7x7) with the flag for position information of cloud which

derived the cloud pixels for total weighting value in the 7x7 matrix. It is designed to provide with QC

up to 1-10 for cloud aspect ratio(H/D).


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Table 3.QC Flag.

CLA - CA

bit Bit Interpretation Field Description

8~5 (Pixel weights in terms of the cone zenith angle.)

unavail => 0

240 224 208 192 176 160 144 128 112 96

0~0.1 0.1~0.2 0.2~0.3 0.3~0.4 0.4~0.5 0.5~0.6 0.6~0.7 0.7~0.8 0.8~0.9 0.9~1

4~1 (Estimated Cloud aspect ratio)

unavail => 0

10 9 8 7 6 5 4 3 2 1

0~0.1 0.1~0.2 0.2~0.3 0.3~0.4 0.4~0.5 0.5~0.6 0.6~0.7 0.7~0.8 0.8~0.9 0.9~1

3.4 Validation

3.4.1 Validation method

Satellite observation CA and ground-observed CA retrieved from COMS are performed in two

ways. First is operational validation by CMDPS.Second, it has itsown validation to see the

effectiveness of the algorithm This method needs the averaging process 30kmx30km(6x6 pixels) to

adjust the spatial resolution.

In the case of ground-observed CA, after it adjusts time information between location information

of ground-observed CA and of satellite observation CA, validating the effectiveness of product on R,

Bias, and RMSE between the two products.


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3.4.2 Validation data

(1) CMDPS Validation (COLL/VAM)

The data used to validate CMDPS satellite-observed used the data of CA MODIS Terra and Aqua

and ground-observed CA validated from November 1 to November 5 using Ground and GTS data. To

determine the weakness of the algorithm, we calculated the statistic (Correlation, BIAS, RMSE)

value dividing into each latitude (the equator : below latitude 30°, middle latitude: the South-North:

30-60°). Also, in CA, unlike in other products, the validation performed included clear sky.

(2) Developer validation

In order to validate satellite observation CA, MODIS cloud product (MOD06) with spatial

resolution(270x406 pixels) of 5x5 km is needed. In the case of ground-observed CA, we used ground

observation data in the SGP of the Atmospheric Radiation Measurement (ARM) program (available online

at http://www.arm.gov). In the case of ARM data, the total sky imager(TSI) was used for measuring

sky cover. It can derive more objective ground observation CA. We validated the effectiveness based

on data from August to December 2002 in the SGP site located mid-latitude similar to the location of

our country.

We used it to validate ground-observed CA at KMA, in Korea for a one month period in December

2007.

3.4.3 Temporal and spatial collocation method

(1) CMDPS Validation (COLL/VAM)

To validate thesatellite observation CA algorithm, we excluded the validation in high latitude

regions above 60° north and south.

We excluded the validation that is represented the difference more than 1-standard deviation of 5x5

pixels in MODIS to validate in the case of homogeneous position for temporal and spatial collocation

Ground data signifies the ground-observed CA at KMA. GTS are compared with cloud amount

entering within ±0.1°the observed data every hour on the hour from ground observation data. In the

case of the ground-observed CA, data more than 20 % are assumed to be navigation and cloud

detection errors and are compared with pixels within 20% for line equivalent of 1:1.

http://www.arm.gov/�


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We corresponded with temporal and spatial collocation to validate ground-observed CA retrieved

through this algorithm and ground satellite CA in the SGP site. We compared average ground-

observed CA retrieved through satellite observation CA and the algorithm to enter within ±30 minute,

±0.1 degreefrom the location of ground observation.

3.4.4 Validation result analysis

(1) CMDPS validation (COLL/VAM)

Table 4. Validation results of CF and CA

Reference Time Region R Bias RMSE

CF

MOD

11/1~11/5

Global 0.85 -3.57 21.68

MYD 0.81 -3.83 22.30

MOD Low

0.85 -5.50 21.48

MYD 0.81 -5.84 23.56

MOD Mid

0.77 -1.52 21.68

MYD 0.78 -1.86 20.90

CA

GTS

11/1~11/5

Global 0.95 0.18 0.81

GROUND 0.75 0.61 1.95

GTS Low

0.95 0.23 0.83

GROUND - - -

GTS Mid

0.91 0.19 0.78

GROUND 0.75 0.61 1.95

Table 4 shows the results in comparison with CA derived from satellite observation CA of MODIS,

Ground, and GTS. First, it shows high correlation coefficients over 0.8 in all regions if we look at the

results of satellite observation CA. When we look at the results of validation by latitude, the difference

of latitude did not influence the results of validation and it was the same as the ground-observed CA.


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The results show better correlation coefficients for GTS observation results than ground. In the

case of ground, as the results observed by ground-observed CA, the objective reliability must be given

attention to decline further than GTS.


Figure 4.shows the relationship between ground observation CA of SGP site and satellite

observation CA derived from MODIS(a), and CA converted through the algorithm, respectively.

According to Berendes et al.(2004), a disparity of within ±20 % represents “good agreement" between

Satellite and Ground observation CA.

As shown in Figure 4, the results show a correlation coefficients of 0.96 between ground and

satellite observation CA within ±20 % and represent a correlation coefficients 0.98 of Ground-

observed CA converted through ground observation CA and the algorithm.

When the overlapped large circles in Figure 4(b) compared with ground observation CA, the

percentage of the improved case for CA retrieved through the algorithm was found to be 53%.

Fig. 4. Validation results of cloud fraction(a) and sky cover(b) for the SGP site. Larger circles in b represent

improvedNhemisphvalues compared with Nnadir

in the site.

Figure 5 shows the difference between CA converted for ground and satellite observation CA, and

ground observation CA and algorithm. The result is a relatively underestimated CA in ground

observation. Thin clouds are widely distributed in mid-latitude region. ground-observed CA at the


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ground seem to not exactly measure for thin cirrus.To convert ground-observed CA for algorithm from

results of Fig. 4 and Fig. 5, the correlation coefficients with ground observation CA is high and the

difference is decreased with real ground observation CA. CA retrieved through this algorithm and

ground-observed CA for one month, December at KMA, the correlation coefficient and bias are 0.61,

0.57, and RMSE denotes 3.17. This results shows that this algorithm is valid on the Korean peninsula

for smaller than RMSE (0.84) in the SGP region.

Fig. 5.monthly mean bias between ground measured cloud fraction and MODIS retrieved cloud fraction

(algorithm-retrieved sky cover).

4. Analysis method of retrieval results

The CA indicates the percentage of precision and accuracy are 10.

Table 5.Detailed Output data for the CA algorithm.

OUTPUT DATA

Parameter Mnemonic Units Min Max Prec Acc Res To

Mean cloud vertical size mean_cld_vert pixel MCV

Mean cloud horizontal size mean_cloud_hori pixel MCH

Cloud aspect ratio cld_asp_ratio 0 10 CASR

Ratio of cone zenith angles pos_weight 0 1 RA

Cloud Fraction Cloud_fraction % 0 100 pixel CF


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Sky cover sky_cover % 0 100 10 10 20km CA

Prec: Precision, Acc: Accuracy, Res: Resolution

5. Problems and possibilities for improvement

In this algorithm, on account of cloud detection and CTP results used as input data, the accuracy of

this algorithm depends on the accuracy of this input data. In general, in the case of ground observation,

it does not measure thin cirrus at high altitudes and can affect ground-observed CA.

Table 6.Quality test result for the CA algorithm.

Quality flag

Parameter bit Value Meaning

sky cover 7 from 0 up to100 ; step: 1 undefined

define illumination and viewing conditions

3

0 undefined

1 night

2 twilight

3 day

4 sunglint

describe the quality of the processing itself

2

0 non processed

1 good quality

2 poor quality

3 bad quality


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6. References

Berenders, T. A., Berendes, D. A., Welch, R. M., IEEE, Member, Dutton, E. G., Uttal, T. and

Clothiaux, E. E., 2004: Cloud cover comparisons of the MODIS Daytime cloud mask with surface

instruments at the north slope of Alaska ARM site, IEEE Transactions on Geoscience and Remote

Sensing, 42, 2584-2593.

Kassianov, E. I., Long, C. N. and Ovtchinnikov, M. 2005: Cloud sky cover versus cloud fraction:

whole-sky simulations and observations, Journal of Applied Meteorology, 44, 86-98.

Oh, H. R., Choi, Y. S., Ho, C. H. and Ahn, M. H., 2006: Development of sky cover retrieval algorithm

for the Communication, Ocean and Meteorological Satellite (COMS) imagery, Journal of Korean

Meteorology Society, 42, 389-396.


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7. Appendix


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Documents

CA Algorithm Theoretical Basis Document - KMA · 2019-07-18 · In order to acquire high qualitydata , The digitized retrieval of ground observation CA is requested through satellites